dingyanna / ML_Project

Augmented KPGNN

This project builds on the code for the Web Conference 2021 paper Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs.

To run KPGNN

Step 1) cd /KPGNN

Step 2) run generate_initial_features.py to generate the initial features for the messages (please see Figure 1(b) and Section 3.2 of the paper for more details).

Step 3) run custom_message_graph.py to construct incremental message graphs. To construct small message graphs for test purpose, set test=True when calling construct_incremental_dataset_0922(). To use all the messages (see Table. 4 of the paper for a statistic of the number of messages in the graphs), set test=False.

Step 4) run main.py

To run KPGNN augmented with GODE

python3 main.py --encoder 1

To run KGPNN augmented with KL-divergence loss

python3 main.py --use_kl 1